Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching,that is,the problem of matching place names that share a common referent.In this articl...Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching,that is,the problem of matching place names that share a common referent.In this article,we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task.We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics,which has the natural advantage of avoiding the manual tuning of similarity thresholds.Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small,and that carefully tuning the similarity threshold is important for achieving good results.The methods based on supervised machine learning,particularly when considering ensembles of decision trees,can achieve good results on this task,significantly outperforming the individual similarity metrics.展开更多
基金the Trans-Atlantic Platform for the Social Sciences and Humanities,through the Digging into Data project with reference HJ-253525also through the Reassembling the Republic of Letters networking programme(EU COST Action IS1310)+1 种基金The researchers from INESC-ID also had financial support from Fundação para a Ciência e a Tecnologia(FCT),through project grants with references PTDC/EEI-SCR/1743/2014(Saturn)CMUP-ERI/TIC/0046/2014(GoLocal),as well as through the INESC-ID multi-annual funding from the PIDDAC programme(UID/CEC/50021/2013).
文摘Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching,that is,the problem of matching place names that share a common referent.In this article,we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task.We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics,which has the natural advantage of avoiding the manual tuning of similarity thresholds.Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small,and that carefully tuning the similarity threshold is important for achieving good results.The methods based on supervised machine learning,particularly when considering ensembles of decision trees,can achieve good results on this task,significantly outperforming the individual similarity metrics.
